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Computational Statistics & Data Analysis
Volume 52, Issue 1, 15 September 2007, Pages 394-405
 
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doi:10.1016/j.csda.2007.02.014    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Elsevier B.V. All rights reserved.

Regularized linear and kernel redundancy analysis

Yoshio TakaneCorresponding Author Contact Information, a, E-mail The Corresponding Author and Heungsun Hwanga, E-mail The Corresponding Author

aDepartment of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, Que., Canada H3A 1B1

Available online 5 March 2007.

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Abstract

Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures redundant information in the criterion variables in a most parsimonious way. A ridge type of regularization was introduced in RA to deal with the multicollinearity problem among the predictor variables. The regularized linear RA was extended to nonlinear RA using a kernel method to enhance the predictability. The usefulness of the proposed procedures was demonstrated by a Monte Carlo study and through the analysis of two real data sets.

Keywords: Ridge regression; Reduced rank approximation; Generalized singular value decomposition (GSVD); Kernel methods; Gaussian kernel; Permutation tests; J-fold cross validation; Bootstrap method

Article Outline

1. Introduction
2. The method
2.1. Linear RA
2.2. Kernel RA
2.3. The choice of dimensionality, λ, and σ
3. Empirical demonstrations
3.1. A Monte Carlo study
3.2. Car attributes and preferences
3.3. Food and cancer data
4. Concluding remarks
Acknowledgements
References




 
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